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Over the past several years the Microbiome Insights team has invested in the development of new tools and techniques for obtaining high-quality, actionable skin microbiome data for our partners and clients in the cosmetics and dermatology industry.

When designing a new skin microbiome study, we always have an important discussion: which variable region should be sequenced? Although many assume that, for characterizing skin bacteria, primers targeting regions V1-3 are superior to those targeting the V4 region, it’s not so straightforward.

All current primers have their limitations—namely, that they underestimate the abundance of some skin-dwelling bacteria, poorly capturing skin commensals.

Our team members Pedro Dimitriu and Hilary Leung redesigned the V4 primer pair under the direction of Microbiome Insights co-founder Dr. Bill Mohn, and found that the new primers resulted in the detection of more bacterial genera, while improving error rates. The new primer also addressed a main limitation of common primers used for the v4 region: it can detect Propionibacterium acnes—the most abundant human skin bacterium.

Thus, we are now pleased to offer our clients this exclusive V4_skin primer in order to help them make the most of their skin microbiome surveys.

In this post, the second of a 2-part series on skin microbiome research, we will discuss technical issues surrounding sequencing of human skin microbes. Read the first blog post here.

At this point, the microbial ecologist conducting a skin microbiome study has now collected all the skin samples she needs, and the DNA has been extracted. We turn to the question of how to decide on the sequencing strategy. Metagenome shotgun sequencing, in which the entire community of microbes is sequenced in an untargeted manner, can provide invaluable information about the functional potential of the microbiome, but – despite continually dropping sequencing costs – it is still expensive. The researcher in this case settles for 16S marker gene sequencing, which targets a specific region of the gene. Now, which primer pair should she choose?

The current dogma in the field is that primers targeting regions V1-V3 are better at describing skin bacterial communities than the V4 region primer pair. (The V4 region is commonly used for studying gut communities and other environments.) This is because V1-V3-sequenced communities better recapitulate the taxonomic composition and relative abundance of “mock community” controls (Meisel et al., 2016). And V4 primers poorly amplify typical skin microbes, notably Propionibacterium and some Staphylococcus species (Meisel et al., 2016). But should V4 be discarded in favor of V1-V3?

The reason behind the V4 region’s underestimation of Propionibacterium is a single mismatch at the end of the primer that prevents efficient binding to a specific group of bacteria. To evaluate if V4 region may be a suitable target for characterizing skin bacteria, our team re-designed the V4 primer pair and tested in silico its ability to improve the coverage of underrepresented propionibacteria. With these new candidate primers, we are able (theoretically) to increase the coverage of Propionibacterium to over 67%–from less than 3%–without losing coverage of the other bacterial groups. Our next step is to evaluate the accuracy of this approach using a mock community as the standard.

There are advantages to using existing V4 primers. They can detect the genera Finegoldia and Peptoniphilus, which are increased in persons with primary immunodeficiencies (Oh et al ., 2013). Zeeuwen et al., citing previous work, also pointed out that the 27F primer used for the V1-V3 region inefficiently amplifies Gardnerella and Lactobacillus, which have been found to be associated with females (Zeeuwen et al., 2012). In general, V1-V3 classifies fewer populations down to the genus level (Meisel et al., 2016). Because the V1-V3 region is longer than the V4 region, paired-end reads generated with the Illumina MiSeq will not fully overlap. And without full overlap, denoising of reads is not as effective. Using the V3 chemistry (a 600-cycle kit, longer than the 500-cycle kit of the V2 version) will not solve the problem and may even make it worse, because the sequence quality drops after 500 cycles.

In this post, the first of a 2-part series on skin microbiome research, we will discuss technical issues surrounding sampling of human skin microbes.

Let’s say a researcher sets out to study bacteria on the human skin—the body’s largest organ, which is teeming with microbes from each domain of life, and viruses. The scientific question has been identified and the funding to conduct a pilot study has been secured. Perhaps her group has some experience studying the gut microbiome. For the most part, she has discovered, getting bacteria out of stool is not too difficult; a small amount of material contains enough microbial DNA to sequence the most prominent members of the microbiome. But unlike the intestine, the skin does not support a high-biomass microbiome. If microbes on the skin are present in low abundance, how does our researcher decide on reasonable sampling and sequencing strategies that together capture a representative picture of bacterial diversity?

Before collecting samples, the researcher must consider key advantages and limitations of available sampling protocols. Commonly used methods involve variations of swabbing – the repeated rubbing of a defined area of the skin with a sterile, pre-moistened swab. When the objective is to obtain sufficient microbial DNA from skin sites with variable and/or low microbial biomass, swabbing can be complemented with scalpel scrapping (Oh et al., 2014). If access to deeper layers, including the dermis, is required, punch biopsies are a viable alternative, but require specialized expertise and are more invasive, reducing the number of sites that can be sampled from the same subject.

Because each method samples a slightly different environment, we would expect different microbial profiles to arise from variations in sampling method. This prediction has not been thoroughly tested (but see Chng et al., 2016). As part of ongoing efforts to improve sampling methods, the Microbiome Insights team is currently evaluating whether D-Squame and Sebutape tape stripping, used for peeling off epidermal layers and sebum, respectively, provide a reliable means of sampling skin microbes. Compared with swabbing, tape stripping recovers ~2- to 3-fold less bacterial DNA. We have yet to evaluate, via amplicon sequencing, whether lower bacterial yield results in different microbial profiles. We are also exploring if coating pre-wetted swabs with aluminum oxide particles maximizes bacterial DNA recovery. The results of this experiment will be made available in a forthcoming technical note.

Handling samples with low microbial biomass is challenging. Even if the sampling method affects how much microbial biomass is collected, the amount of DNA recovered from skin is always low. (Of course, the DNA extraction method affects DNA yield and microbial composition from study to study. But because most studies are comparative in nature, methodological consistency is vitally important.) As most of the DNA is human, obtaining enough genetic material for microbial profiling can be difficult. Microbial load can be increased by instructing participants not to wash with soap or bathe at least 24 hrs prior to sampling, although the effect of cleansing is probably minor compared with the combined influence of sampling and extraction.

Another challenge of low microbial biomass samples is dealing with environmental contamination. Contamination can be introduced during sample collection, DNA extraction, and sequencing library preparation. For instance, bacterial DNA is often found in DNA extraction kits and in other reagents used for preparing samples. And while it is tempting to create lists of “usual suspect” contaminants, this may be futile when studying skin microbes or other human-associated bacteria because, for example, Staphylococcus, a common skin inhabitant, and Escherichia have been identified as potential kit reagent contaminants (Salter et al., 2014).

Processing negative controls alongside low-biomass specimens is critical, because the proportion of microbial DNA attributable to contamination is higher in low-biomass samples compared with high-biomass samples. Usually, we include at least four replicates for each of two types of negative controls on each 16S sequencing run: (1) DNA extraction controls, to assess if kit reagents carry a detectable signal, and (2) template-free PCR blanks, to pinpoint contamination that may arise during downstream processing. For skin microbiome analysis, sterile swabs opened at the site of sample collection are co-processed with the swabbed samples. In general, the number of sequencing reads in our negative controls is about 3- to 4-fold lower than the average in samples derived from skin sites. This is what we would expect for samples containing little to no DNA.

Stay tuned for the second post in this series: amplicon sequencing in skin microbiome studies.